A Generative Meta‐Atom Model for Metasurface‐Based Absorber Designs

Abstract Metasurfaces (MS) are widely accepted in the devices, such as absorbers, to improve their performances. MS provide new design freedoms, however, they also result in complexity in designs because of the high‐dimensional topological space of meta‐atoms, and high computational costs in the optimization procedure. To alleviate this challenge, a generative meta‐atom model that generates the pattern and corresponding electromagnetic (EM) responses of meta‐atoms, specialized for absorbing applications is developed. The model is established by the convolutional variational autoencoder (CVAE), and a deep neural network (DNN). The model is verified by designing different types of absorbers with the evolutionary algorithm, and an ultrabroadband lower profile absorber at low microwave frequencies is achieved. The realized metasurface‐based absorber (MSA) covers the frequency range from 1.4 to 18 GHz at the criteria of return loss (RL) −10 dB with a thickness of 8 mm, which is validated by experiments. This work provides an effective and highly efficient way to design high‐performance MSA, which can be easily extended to other metasurface‐based functional devices.